Job Description

The successful candidate will work with Asst. Prof. Marc Hon and Prof. Anthony Tung from the NUS School of Computing on an interdisciplinary NUS Physics × Computing project: AI-Driven Discovery of Dark Matter Candidates via Gravitational Microlensing. 

The responsibilities of the position include:

• Designing methods for anomaly detection in irregularly sampled time-series of stellar brightness over time
• Physics-aware generation of time-series data containing transient events
• Coordination with Physics and Computing teams for applications to next-generation astronomical surveys

The role is ideal for someone with a strong computational background combined with an interest in time-domain AI and scientific machine learning. Expertise in astrophysics can be developed during the project.

Qualifications

Bachelor’s Degree in Data Science, Computer Science, Physics, or a similar quantitative field.

Skills:

• Strong analytical and computational skills in data science, statistical analysis, or machine learning.
• Proficiency with PyTorch and data analysis frameworks in Python.
• Familiarity with large, complex datasets and modern computational techniques.
• Good written and oral scientific communication skills.

Experience:

• Demonstrated experience conducting independent research involving the analysis of large datasets.
• Familiarity with modern machine learning methodologies, particularly for time-series data.
• A record of relevant projects involving temporal data and/or time-series analyses is desirable.